Cardiac diffusion tensor imaging (DTI) offers unique insights into cardiomyocyte arrangements, bridging the gap between microscopic and macroscopic cardiac function. However, its clinical utility is limited by technical challenges, including a low signal-to-noise ratio, aliasing artefacts, and the need for accurate quantitative fidelity. To address these limitations, we introduce RSFR (Reconstruction, Segmentation, Fusion & Refinement), a novel framework for cardiac diffusion-weighted image reconstruction. RSFR employs a coarse-to-fine strategy, leveraging zero-shot semantic priors via the Segment Anything Model and a robust Vision Mamba-based reconstruction backbone. Our framework integrates semantic features effectively to mitigate artefacts and enhance fidelity, achieving state-of-the-art reconstruction quality and accurate DT parameter estimation under high undersampling rates. Extensive experiments and ablation studies demonstrate the superior performance of RSFR compared to existing methods, highlighting its robustness, scalability, and potential for clinical translation in quantitative cardiac DTI.
View on arXiv@article{huang2025_2504.18520,
title={ RSFR: A Coarse-to-Fine Reconstruction Framework for Diffusion Tensor Cardiac MRI with Semantic-Aware Refinement },
author={ Jiahao Huang and Fanwen Wang and Pedro F. Ferreira and Haosen Zhang and Yinzhe Wu and Zhifan Gao and Lei Zhu and Angelica I. Aviles-Rivero and Carola-Bibiane Schonlieb and Andrew D. Scott and Zohya Khalique and Maria Dwornik and Ramyah Rajakulasingam and Ranil De Silva and Dudley J. Pennell and Guang Yang and Sonia Nielles-Vallespin },
journal={arXiv preprint arXiv:2504.18520},
year={ 2025 }
}